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IPSM-Bench: A New Intermediate Phase Segmentation Benchmark in Microstructure Images of Zinc-Based Absorbable Biomaterials

arXiv 2606.11001 ↗First posted 9 Jun 2026cs.CVcited by 0

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Abstract (arXiv)

Zinc-based alloys are indispensable emerging absorbable metallic biomaterials, and their macroscopic performance is governed by microstructural characteristics. Intermediate phases-key microstructural constituents-are pivotal in regulating mechanical and functional properties. However, intermediate phase segmentation in zinc alloy microstructures faces formidable challenges: scarce annotated datasets, low contrast, difficulty detecting small targets, and heterogeneous morphologies. To this end, we construct IPSM-Bench, the largest high-quality dataset for zinc-alloy intermediate phase segmentation. Furthermore, we propose SCoP-SAM, a new Spatial Context Prior-guided SAM method that leverages the gradient structure and grayscale properties of intermediate phases to capture spatial context priors and incorporates them into the entire SAM encoding-decoding process, improving segmentation performance. Based on the proposed IPSM-Bench, we establish a new benchmark for intermediate phase segmentation to systematically evaluate state-of-the-art (SOTA) methods and advance research on zinc alloy microstructure analysis. Extensive experiments on IPSM-Bench and additional public alloy benchmarks demonstrate that our SCoP-SAM not only achieves SOTA performance for zinc-alloy intermediate phase segmentation but also generalizes remarkably well to other alloy scenarios.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 26 references that are in the map. First posted on arXiv 9 Jun 2026.

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References · 7

  1. 2023Segment Anythingcited by 8,234
  2. 2023Segment Anything Is Not Always Perfect: An Investigation of SAM on Different Real-world Applicationscited by 121
  3. 2024RobustSAM: Segment Anything Robustly on Degraded Imagescited by 36
  4. 2024CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Modelcited by 27
  5. 2018High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steelcited by 19
  6. 2024SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Promptscited by 5
  7. 2024SAM-I-Am: Semantic Boosting for Zero-shot Atomic-Scale Electron Micrograph Segmentationcited by 5

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